{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-qwen-qwen-2.5-coder-32b-instruct","slug":"qwen-qwen-2.5-coder-32b-instruct","name":"Qwen2.5 Coder 32B Instruct","type":"model","url":"https://openrouter.ai/models/qwen~qwen-2.5-coder-32b-instruct","page_url":"https://unfragile.ai/qwen-qwen-2.5-coder-32b-instruct","categories":["code-editors"],"tags":["qwen","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$6.60e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-qwen-qwen-2.5-coder-32b-instruct__cap_0","uri":"capability://code.generation.editing.multi.language.code.generation.with.instruction.tuned.reasoning","name":"multi-language code generation with instruction-tuned reasoning","description":"Generates syntactically correct and semantically sound code across 40+ programming languages (Python, JavaScript, Java, C++, Go, Rust, etc.) using instruction-tuned transformer architecture trained on high-quality code corpora. The model applies chain-of-thought reasoning patterns during generation to decompose complex coding tasks into intermediate steps, improving correctness for multi-step algorithms and architectural decisions. Supports both function-level completion and full-file generation with context awareness up to 32K tokens.","intents":["Generate boilerplate code and scaffolding for new projects or modules","Complete partial code implementations with semantically correct continuations","Implement algorithms from natural language specifications or pseudocode","Generate code in unfamiliar languages based on examples or requirements"],"best_for":["Full-stack developers building features across multiple language ecosystems","Teams migrating codebases between languages and needing translation assistance","Developers prototyping algorithms quickly without memorizing language-specific idioms"],"limitations":["No real-time compilation feedback — generated code may have subtle runtime errors in edge cases","Context window of 32K tokens limits ability to generate code aware of very large existing codebases","No built-in testing or validation — requires external test harnesses to verify correctness","Performance degrades on highly domain-specific or proprietary code patterns not well-represented in training data"],"requires":["API access via OpenRouter or compatible endpoint","Minimum 8GB VRAM for local deployment (32B parameter model)","Input prompt formatted as instruction or code completion context"],"input_types":["natural language specifications","partial code with cursor position","pseudocode or algorithm descriptions","code comments describing intent"],"output_types":["complete function implementations","multi-file code structures","code snippets with explanatory comments","refactored or optimized versions of input code"],"categories":["code-generation-editing","multi-language-support"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-2.5-coder-32b-instruct__cap_1","uri":"capability://code.generation.editing.code.reasoning.and.explanation.with.architectural.awareness","name":"code reasoning and explanation with architectural awareness","description":"Analyzes existing code to explain logic, identify design patterns, and reason about correctness using transformer-based semantic understanding of code structure. The model recognizes architectural patterns (MVC, factory, observer, etc.), dependency relationships, and control flow without requiring explicit AST parsing, instead learning these patterns from training data. Produces explanations at multiple abstraction levels: line-by-line logic, function-level intent, and system-level architecture.","intents":["Understand unfamiliar codebases or legacy code without documentation","Identify design patterns and architectural decisions in existing implementations","Explain why specific code patterns were chosen and their trade-offs","Reason about code correctness and potential bugs or edge cases"],"best_for":["Teams onboarding new developers to complex codebases","Code reviewers needing to understand architectural intent quickly","Developers maintaining legacy systems with minimal documentation","Technical leads evaluating code quality and design decisions"],"limitations":["Explanations may miss context-specific business logic not evident from code alone","Cannot reason about runtime behavior without execution traces or profiling data","May misidentify patterns in highly unconventional or obfuscated code","Explanations are generated text, not executable analysis — require human verification"],"requires":["Code input formatted as plain text or within prompt context","API access via OpenRouter or compatible endpoint","Code samples under 32K tokens for full context understanding"],"input_types":["source code in any language","code snippets with surrounding context","pseudocode or algorithm descriptions","architecture diagrams described in text"],"output_types":["natural language explanations","design pattern identification","architectural analysis","correctness reasoning and potential issues"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-2.5-coder-32b-instruct__cap_2","uri":"capability://code.generation.editing.code.debugging.and.error.diagnosis.with.fix.suggestions","name":"code debugging and error diagnosis with fix suggestions","description":"Identifies bugs, runtime errors, and logical flaws in code by analyzing error messages, stack traces, and code context together. The model correlates error symptoms with root causes using patterns learned from debugging datasets, then generates targeted fix suggestions with explanations of why the bug occurred. Supports both syntax errors (caught at parse time) and semantic/logic errors (runtime or behavioral issues), with suggestions ranging from one-line fixes to architectural refactors.","intents":["Diagnose runtime errors from stack traces and error messages","Identify logical bugs in algorithms or control flow","Suggest fixes for common programming mistakes and anti-patterns","Explain why a bug occurred and how to prevent similar issues"],"best_for":["Developers debugging issues in unfamiliar codebases or languages","Teams reducing time-to-resolution for production incidents","Junior developers learning debugging patterns and best practices","CI/CD pipelines needing automated error diagnosis and suggestions"],"limitations":["Requires sufficient context (error message + surrounding code) — vague errors may produce generic suggestions","Cannot diagnose issues requiring runtime state inspection or memory profiling","May suggest fixes that work locally but fail in production due to environment differences","No guarantee that suggested fixes are optimal or follow project-specific coding standards"],"requires":["Error message or stack trace in input","Relevant code context (function or file containing the error)","API access via OpenRouter or compatible endpoint"],"input_types":["error messages and stack traces","source code with bug location","reproduction steps or test cases","logs or debug output"],"output_types":["root cause analysis","code fixes with diffs","explanations of why the bug occurred","prevention strategies and best practices"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-2.5-coder-32b-instruct__cap_3","uri":"capability://code.generation.editing.code.refactoring.with.style.and.performance.optimization","name":"code refactoring with style and performance optimization","description":"Transforms code to improve readability, maintainability, and performance while preserving functionality. The model applies refactoring patterns (extract method, rename variables, simplify conditionals, etc.) learned from high-quality code examples, and suggests optimizations based on algorithmic complexity and language-specific idioms. Generates refactored code with explanations of trade-offs (e.g., readability vs. performance) and can target specific style guides or frameworks.","intents":["Improve code readability and maintainability without changing behavior","Optimize performance-critical sections based on algorithmic improvements","Modernize legacy code to use current language features and idioms","Enforce coding standards and style guides across a codebase"],"best_for":["Teams conducting code quality initiatives or technical debt reduction","Developers modernizing legacy codebases to current language versions","Performance engineers optimizing hot paths without manual analysis","Projects adopting new style guides or architectural patterns"],"limitations":["Refactoring suggestions may not account for project-specific constraints or performance requirements","Cannot verify that refactored code maintains exact behavioral equivalence without test execution","May suggest over-engineered solutions for simple code","Optimization suggestions are heuristic-based, not guaranteed to improve performance in all contexts"],"requires":["Source code input in supported language","Optional: style guide or refactoring preferences in prompt","API access via OpenRouter or compatible endpoint"],"input_types":["source code in any language","style guide specifications","performance requirements or constraints","refactoring objectives (readability, performance, modernization)"],"output_types":["refactored code with preserved functionality","explanations of changes and trade-offs","performance improvement estimates","migration guides for major refactors"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-2.5-coder-32b-instruct__cap_4","uri":"capability://code.generation.editing.test.case.generation.and.test.driven.development.support","name":"test case generation and test-driven development support","description":"Generates unit tests, integration tests, and edge case test suites from code specifications or existing implementations. The model identifies critical paths, boundary conditions, and error scenarios using code analysis patterns, then generates test code in the appropriate framework (pytest, Jest, JUnit, etc.). Supports test-driven development workflows by generating tests from requirements before implementation, and can generate fixtures, mocks, and test data.","intents":["Generate comprehensive test suites for existing code with high coverage","Create test cases from requirements before implementing features (TDD)","Identify and test edge cases and error conditions","Generate test fixtures, mocks, and test data for integration testing"],"best_for":["Teams improving test coverage and code quality metrics","Developers practicing test-driven development","Projects with strict quality requirements (financial, healthcare, safety-critical)","Teams onboarding to new testing frameworks or patterns"],"limitations":["Generated tests may not cover all business logic edge cases without human review","Test quality depends on clarity of code or requirements — ambiguous specs produce weak tests","Cannot generate tests for non-deterministic or time-dependent behavior without additional context","May generate tests that pass but don't validate actual correctness (false positives)"],"requires":["Source code or requirements specification","Target testing framework specified (pytest, Jest, JUnit, etc.)","API access via OpenRouter or compatible endpoint"],"input_types":["source code implementations","requirements or specifications","API contracts or interfaces","error scenarios or edge cases to test"],"output_types":["unit test code in target framework","test fixtures and mocks","test data generators","integration test scenarios"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-2.5-coder-32b-instruct__cap_5","uri":"capability://text.generation.language.api.and.function.signature.documentation.generation","name":"api and function signature documentation generation","description":"Generates comprehensive documentation for APIs, functions, and classes by analyzing code signatures, implementations, and usage patterns. The model produces docstrings in multiple formats (JSDoc, Sphinx, Google-style, etc.), generates parameter descriptions with type information, and creates usage examples. Supports generating documentation from code-first or spec-first approaches, and can infer documentation from type hints and implementation details.","intents":["Generate API documentation from code without manual writing","Create docstrings and parameter descriptions for functions and classes","Generate usage examples and common patterns for APIs","Maintain documentation consistency across large codebases"],"best_for":["Teams improving code documentation coverage and quality","Open-source projects needing comprehensive API documentation","Developers maintaining large libraries or frameworks","Teams enforcing documentation standards across codebases"],"limitations":["Generated documentation may miss domain-specific context or business logic intent","Cannot infer non-obvious parameter constraints or side effects without explicit comments","Documentation quality depends on code clarity — poorly written code produces poor documentation","May generate overly verbose or redundant documentation for simple functions"],"requires":["Source code with function/class signatures","Target documentation format specified (JSDoc, Sphinx, Google-style, etc.)","API access via OpenRouter or compatible endpoint"],"input_types":["function and class signatures","type hints and annotations","implementation code","existing partial documentation"],"output_types":["docstrings in target format","parameter and return type descriptions","usage examples","API reference documentation"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-2.5-coder-32b-instruct__cap_6","uri":"capability://code.generation.editing.code.review.assistance.with.quality.and.security.analysis","name":"code review assistance with quality and security analysis","description":"Analyzes code changes to identify potential issues, security vulnerabilities, performance problems, and style violations. The model applies code review heuristics learned from high-quality review datasets, checking for common anti-patterns, security risks (SQL injection, XSS, buffer overflows, etc.), and architectural concerns. Provides actionable feedback with severity levels and suggestions for improvement, supporting both automated pre-review scanning and interactive review assistance.","intents":["Identify security vulnerabilities and common attack vectors in code changes","Detect performance issues and algorithmic inefficiencies","Flag code style violations and architectural anti-patterns","Provide actionable feedback for code improvement before human review"],"best_for":["Teams implementing security-first development practices","Projects with strict code quality and security requirements","Developers learning code review best practices","CI/CD pipelines needing automated pre-review quality gates"],"limitations":["Security analysis is pattern-based, not exhaustive — may miss novel or context-specific vulnerabilities","Cannot detect vulnerabilities requiring runtime analysis or external dependency analysis","Performance analysis is heuristic-based, not backed by profiling data","May flag false positives for legitimate code patterns or project-specific conventions"],"requires":["Code changes or diffs in input","Context about the codebase (language, framework, security requirements)","API access via OpenRouter or compatible endpoint"],"input_types":["code diffs or pull request changes","full file context for analysis","security requirements or compliance standards","performance constraints or SLAs"],"output_types":["code review comments with severity levels","security vulnerability reports","performance improvement suggestions","architectural feedback and recommendations"],"categories":["code-generation-editing","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-2.5-coder-32b-instruct__cap_7","uri":"capability://code.generation.editing.natural.language.to.code.translation.with.context.preservation","name":"natural language to code translation with context preservation","description":"Converts natural language specifications, requirements, or pseudocode into executable code while preserving intent and context. The model maps natural language descriptions to code constructs, infers data structures and algorithms from requirements, and generates idiomatic code in the target language. Supports iterative refinement through follow-up questions and clarifications, and can generate code at multiple abstraction levels (high-level architecture, detailed implementation, or specific functions).","intents":["Implement features from natural language requirements or user stories","Convert pseudocode or algorithm descriptions into working code","Translate specifications from non-technical stakeholders into code","Rapidly prototype features from high-level descriptions"],"best_for":["Non-technical stakeholders or product managers prototyping features","Teams rapidly prototyping features from requirements","Developers implementing complex algorithms from academic papers or specifications","Cross-functional teams bridging communication gaps between technical and non-technical members"],"limitations":["Generated code may not match project-specific patterns or architectural conventions","Ambiguous requirements produce code that works but may not match intent","Cannot infer non-functional requirements (performance, scalability, security) without explicit specification","Requires iterative refinement for complex features — single-pass generation often produces incomplete solutions"],"requires":["Natural language specification or requirements","Target programming language specified","API access via OpenRouter or compatible endpoint"],"input_types":["natural language requirements or user stories","pseudocode or algorithm descriptions","high-level feature descriptions","API contracts or interface specifications"],"output_types":["executable code in target language","architectural designs or module structures","implementation with explanatory comments","clarifying questions for ambiguous requirements"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-2.5-coder-32b-instruct__cap_8","uri":"capability://code.generation.editing.code.migration.and.language.translation.with.semantic.equivalence","name":"code migration and language translation with semantic equivalence","description":"Translates code from one programming language to another while preserving functionality and adapting to target language idioms. The model maps language-specific constructs (e.g., Python list comprehensions to JavaScript array methods), adapts standard library calls, and generates idiomatic code in the target language. Supports both direct translation (line-by-line mapping) and semantic translation (restructuring for target language patterns), with explanations of design decisions and potential behavioral differences.","intents":["Migrate codebases between programming languages (e.g., Python to Go, Java to Rust)","Port algorithms or libraries to new languages","Adapt code to different runtime environments or platforms","Learn new languages by translating familiar code patterns"],"best_for":["Teams migrating between technology stacks or languages","Projects porting libraries or frameworks to new platforms","Developers learning new languages by translating existing code","Organizations consolidating polyglot codebases"],"limitations":["Translated code may not leverage target language idioms or best practices optimally","Language-specific features (e.g., Python decorators, Rust ownership) may not translate directly","Performance characteristics may differ significantly between languages — translation doesn't guarantee equivalent performance","Requires human review to ensure translated code follows project conventions and passes tests"],"requires":["Source code in supported language","Target programming language specified","API access via OpenRouter or compatible endpoint"],"input_types":["source code in any supported language","language-specific idioms or patterns to preserve","target language version or framework","performance or compatibility constraints"],"output_types":["translated code in target language","explanations of translation decisions","notes on behavioral differences or incompatibilities","migration guides for complex translations"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-2.5-coder-32b-instruct__cap_9","uri":"capability://text.generation.language.interactive.coding.assistant.with.multi.turn.conversation","name":"interactive coding assistant with multi-turn conversation","description":"Provides real-time coding assistance through multi-turn conversation, maintaining context across multiple exchanges to support iterative development. The model tracks conversation history, understands follow-up questions and refinements, and adapts suggestions based on previous interactions. Supports clarifying questions, requesting alternative approaches, and progressively building complex solutions through dialogue.","intents":["Get real-time coding help and suggestions while developing","Iteratively refine code through conversation and feedback","Ask follow-up questions and explore alternative approaches","Maintain context across multiple coding tasks in a session"],"best_for":["Individual developers seeking real-time coding assistance","Pair programming scenarios with AI as a collaborative partner","Learning and skill development in new languages or frameworks","Exploratory development where requirements evolve through conversation"],"limitations":["Context window of 32K tokens limits conversation history — very long sessions may lose earlier context","Model may drift from original intent if conversation becomes too exploratory","No persistent memory across sessions — context resets between conversations","Conversation quality depends on clarity of questions — vague or ambiguous queries produce generic responses"],"requires":["API access via OpenRouter or compatible endpoint","Conversation interface or chat client","Ability to provide code context and feedback in natural language"],"input_types":["natural language questions and requests","code snippets and context","feedback and refinement requests","follow-up questions and clarifications"],"output_types":["code suggestions and implementations","explanations and reasoning","alternative approaches and trade-offs","clarifying questions and suggestions"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["API access via OpenRouter or compatible endpoint","Minimum 8GB VRAM for local deployment (32B parameter model)","Input prompt formatted as instruction or code completion context","Code input formatted as plain text or within prompt context","Code samples under 32K tokens for full context understanding","Error message or stack trace in input","Relevant code context (function or file containing the error)","Source code input in supported language","Optional: style guide or refactoring preferences in prompt","Source code or requirements specification"],"failure_modes":["No real-time compilation feedback — generated code may have subtle runtime errors in edge cases","Context window of 32K tokens limits ability to generate code aware of very large existing codebases","No built-in testing or validation — requires external test harnesses to verify correctness","Performance degrades on highly domain-specific or proprietary code patterns not well-represented in training data","Explanations may miss context-specific business logic not evident from code alone","Cannot reason about runtime behavior without execution traces or profiling data","May misidentify patterns in highly unconventional or obfuscated code","Explanations are generated text, not executable analysis — require human verification","Requires sufficient context (error message + surrounding code) — vague errors may produce generic suggestions","Cannot diagnose issues requiring runtime state inspection or memory profiling","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.45,"ecosystem":0.24,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:24.485Z","last_scraped_at":"2026-05-03T15:20:45.776Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=qwen-qwen-2.5-coder-32b-instruct","compare_url":"https://unfragile.ai/compare?artifact=qwen-qwen-2.5-coder-32b-instruct"}},"signature":"utsby6K4F5SV+GMgOak5Epqy3UtsW4bytAiSswEsmmjj72NDvxBKzVnGQEzmKGTHNRJ9o3XglZn3K3/72BIUDQ==","signedAt":"2026-06-20T15:17:46.816Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/qwen-qwen-2.5-coder-32b-instruct","artifact":"https://unfragile.ai/qwen-qwen-2.5-coder-32b-instruct","verify":"https://unfragile.ai/api/v1/verify?slug=qwen-qwen-2.5-coder-32b-instruct","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}